This paper is in the following e-collection/theme issue:

JMIR Theme Issue: COVID-19 Special Issue (2452) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1041) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1641) Infodemiology and Infoveillance (1349) Natural Language Processing (692) Machine Learning (1465) Discourse (38)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/41319, first published .
Dynamics of the Negative Discourse Toward COVID-19 Vaccines: Topic Modeling Study and an Annotated Data Set of Twitter Posts

Dynamics of the Negative Discourse Toward COVID-19 Vaccines: Topic Modeling Study and an Annotated Data Set of Twitter Posts

Dynamics of the Negative Discourse Toward COVID-19 Vaccines: Topic Modeling Study and an Annotated Data Set of Twitter Posts

Authors of this article:

Gabriel Lindelöf1, 2 Author Orcid Image ;   Talayeh Aledavood1 Author Orcid Image ;   Barbara Keller1 Author Orcid Image
Article Authors Cited by (10) Tweetations (21) Metrics

Journals

  1. Canaparo M, Ronchieri E, Scarso L. A natural language processing approach for analyzing COVID-19 vaccination response in multi-language and geo-localized tweets. Healthcare Analytics 2023;3:100172 View
  2. Kaushal A, Mandal A, Khanna D, Acharjee A. Analysis of the opinions of individuals on the COVID-19 vaccination on social media. DIGITAL HEALTH 2023;9 View
  3. Cheng T, Han B, Liu Y. Exploring public sentiment and vaccination uptake of COVID-19 vaccines in England: a spatiotemporal and sociodemographic analysis of Twitter data. Frontiers in Public Health 2023;11 View
  4. Gu D, Wang Q, Chai Y, Yang X, Zhao W, Li M, Zolotarev O, Xu Z, Zhang G. Identifying the Risk Factors of Allergic Rhinitis Based on Zhihu Comment Data Using a Topic-Enhanced Word-Embedding Model: Mixed Method Study and Cluster Analysis. Journal of Medical Internet Research 2024;26:e48324 View
  5. Soga K, Yoshida S, Muneyasu M. Graph-Based Interpretability for Fake News Detection through Topic- and Propagation-Aware Visualization. Computation 2024;12(4):82 View
  6. Lee Y, Alostad H, Davulcu H. Quantifying Variations in Controversial Discussions within Kuwaiti Social Networks. Big Data and Cognitive Computing 2024;8(6):60 View
  7. Lee H, Kang M, Lee E. Lost in communication: The vanished momentum of just transition in South Korea. Energy Research & Social Science 2024;115:103642 View
  8. Galli C, Cusano C, Guizzardi S, Donos N, Calciolari E. Embeddings for Efficient Literature Screening: A Primer for Life Science Investigators. Metrics 2024;1(1):1 View